Closed-loop Adaptive Battery Management for Lifetime Extension
Eligibility: UK/International (including EU) graduates with the required entry requirements
Duration: Full-Time – Four years fixed term
Application deadline: 15 November 2026
Interview date: Will be confirmed to shortlisted candidates
Start date: May 2027
For further details contact: Cheng Zhang
Introduction
Through SRAP, Coventry-based PhD researchers undertake one to two years of research training in Singapore under the joint supervision of researchers from both Coventry and ARTC. The programme offers PhD researchers an exceptional opportunity to gain international research experience while contributing to ambitious, multidisciplinary research addressing global technological and sustainability challenges.
This collaborative PhD programme enables researchers to work at the interface of academic research, advanced engineering and industrial innovation. Researchers will engage with expertise and facilities across both institutions, build international research networks, and develop experience in translating advanced research into practical solutions for future mobility and remanufacturing systems.
The programme is particularly suited to ambitious researchers seeking to shape next-generation electric mobility systems and circular manufacturing technologies, while gaining valuable research experience across the UK and Singapore.
Project details
Conventional battery management systems are typically designed and calibrated using battery characteristics measured at the beginning of life. However, batteries continuously evolve during operation due to ageing, temperature variations and changing usage conditions. Capacity, resistance, electrochemical characteristics and degradation mechanisms can change significantly over lifetime. As a result, fixed BMS models and control strategies may become increasingly inaccurate and suboptimal as the battery ages, limiting performance, lifetime and safety.
This PhD project will develop a closed-loop adaptive battery management framework that continuously learns the evolving condition of the battery and adapts its estimation and control strategies to maximise useful lifetime.
The proposed framework will integrate battery sensing, state and parameter estimation, ageing diagnosis, model adaptation and optimal control within a closed loop. Operational battery data will be used to identify changes in battery behaviour and update models and parameters throughout life. Particular attention will be given to the challenge of extracting reliable ageing information from limited and noisy field measurements, where battery parameters and degradation modes may be weakly observable.
Based on the estimated battery condition, adaptive control strategies will be developed to modify operating limits, charging profiles, thermal management or power constraints. The objective is to balance immediate performance requirements against long-term degradation, enabling the BMS to actively manage the battery towards improved lifetime rather than simply monitoring its condition.
The research will combine battery experiments, physics-based and data-driven modelling, estimation, optimisation and control. The ultimate aim is to establish the foundations of a self-adapting battery management system capable of learning from battery behaviour and continuously optimising its operation throughout its lifetime.
Research may include
• Experimental characterisation of battery behaviour and degradation under different operating conditions.
• Development of ageing-adaptive battery models and digital twins.
• Online estimation of battery states, health indicators and ageing-dependent parameters.
• Investigation of parameter identifiability and observability using realistic BMS measurements.
• Selection of informative operational data for reliable online model and parameter recalibration.
• Diagnosis of battery degradation modes, such as loss of lithium inventory (LLI), loss of active material (LAM) and resistance growth.
• Development of robust state and parameter estimation methods considering measurement noise, model mismatch and uncertainty.
• Development of closed-loop adaptive charging, power and thermal management strategies.
• Multi-objective optimal control balancing performance, charging time, safety and degradation.
• Ageing-adaptive, plating-aware fast-charging strategies.
• Physics-informed and machine-learning approaches for adaptive battery management.
• Uncertainty quantification and confidence-aware adaptation to prevent unsafe or unreliable model updates.
• Experimental validation of lifetime benefits compared with conventional non-adaptive BMS approaches.
Funding
Tuition fees and bursary
Benefits
The successful candidate will receive comprehensive research training including technical, personal and professional skills. All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities.
You’re invited to become part of the Coventry Centre for Doctoral Training in AI (C-CDT AI ): a dynamic, collaborative hub uniting AI staff and PGR researchers from across the University.
As a C-CDT member you’ll benefit from structured AI training (from core research methods to ethics and innovation), interdisciplinary peer networks, specialist supervision, shared GPU/cloud resources, workshops, seminars and industry-focused challenge projects.
This additional training and community, complements your home research centre programme, accelerates your technical and professional development, and connects you with a broad AI research ecosystem tackling impactful problems across sectors.
Entry requirements
- A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.
PLUS
- The potential to engage in innovative research and to complete the PhD within 4 years.
- A minimum of English language proficiency (IELTS academic overall minimum score of 7.0 with a minimum of 6.5 in each component).
Additional requirements
Applicants should have, or expect to obtain, a good undergraduate or Master's degree in a relevant discipline such as electrical/electronic engineering, control engineering, computer science, automotive engineering, mechanical engineering, or a closely related subject.
A strong interest in battery management, modelling, control and experimental research is essential. Experience in one or more of the following areas would be advantageous: battery testing, battery modelling, state estimation, system identification, optimisation, control, machine learning, signal processing, MATLAB/Simulink, Python or embedded systems.
The successful candidate should be comfortable working across experimental and computational research, including conducting battery tests, analysing experimental datasets, developing estimation and control algorithms, and validating algorithms using battery test facilities.
Good analytical and problem-solving skills, ability to work independently, and effective written and verbal communication skills are expected. Previous battery research experience is desirable but not essential, provided that the applicant has a strong quantitative background and motivation to develop expertise in adaptive battery management.
Candidates should demonstrate a strong interest in interdisciplinary research and an enthusiasm for addressing complex engineering and sustainability challenges.
Candidates should be capable of working independently while also contributing effectively to a multidisciplinary research team. Strong analytical, problem-solving, communication and scientific writing skills are essential.
As the programme involves international collaborative research and an extended research attachment in Singapore, candidates should also demonstrate adaptability, cultural awareness, effective communication and a willingness to work in an international research environment.
Candidates may be required to satisfy the academic, English-language, immigration, security and other eligibility requirements of Coventry University, A*STAR and the Singapore Research Attachment Programme.
How to apply
To find out more about the project, please contact Cheng Zhang
All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.
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